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Research Paper | Computer Science and Information Technology | India | Volume 14 Issue 4, April 2025 | Popularity: 6.2 / 10
Malicious URLs Detection Using Machine Learning Models
Armaanpreet Singh Khehra
Abstract: This research focuses on developing a machine learning - based system for determining malicious links. The main goal is to distinguish between benign and malicious web links using automated classification techniques. To achieve this, we used multiple datasets, including the Malicious URLs Dataset by Manu Siddhartha and the URL Dataset by Antony J. These datasets provide a vast compilation of URLs labeled in different categories, making it easy for effective model training and evaluation. This approach involves training various machine learning algorithms, comprising Random Forest, Gradient Boosting, and Multi - Layer Perceptron models. Feature extraction procedures such as one - hot encoding and URL parsing etc. were put into practice to maximize model?s performance. The trained models achieved an accuracy of 85%, and have shown substantial possibilities for real - world cybersecurity applications. This prototype serves as a promising move toward automated URL threat detection, with future work focusing on improving detection efficiency and managing evolving cyber threats.
Keywords: Malicious URLs, Machine Learning, Cybersecurity, URL Classification, Feature Extraction
Edition: Volume 14 Issue 4, April 2025
Pages: 725 - 731
DOI: https://www.doi.org/10.21275/SR25407123119
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